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000-455 - High Volume Storage Fundamentals V3 - Dump Information

Vendor : IBM
Exam Code : 000-455
Exam Name : High Volume Storage Fundamentals V3
Questions and Answers : 73 Q & A
Updated On : December 18, 2018
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000-455 Questions and Answers

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000-455 High Volume Storage Fundamentals V3

Study Guide Prepared by Killexams.com IBM Dumps Experts


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000-455 exam Dumps Source : High Volume Storage Fundamentals V3

Test Code : 000-455
Test Name : High Volume Storage Fundamentals V3
Vendor Name : IBM
Q&A : 73 Real Questions

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IBM IBM High Volume Storage

using IBM Spectrum Scale for storage in IBM Cloud private | killexams.com Real Questions and Pass4sure dumps

IBM Spectrum Scale is a versatile application-defined storage that can also be deployed as high performance file storage or a cost optimized huge-scale content material repository. IBM Spectrum Scale is deployed on the most traumatic corporations on this planet for each excessive performance and excessive scale. consult with https://www-03.ibm.com/programs/storage/spectrum/scale/ for extra particulars. aspects that make Spectrum Scale appropriate for persistent volume storage include: performance, scalability, suggestions lifecycle management, quota, encryption, compression, availability, and snapshots.

Storage for Containers

some of the leading components interior any cluster is storage. before you can installation most Kubernetes-based mostly purposes, you need to first create storage from the underlying infrastructure. This provisioned storage allows for persistence of an software’s information. although, for an application to devour this storage, you should additionally create a volume. Volumes are used to make requests on behalf of an utility to the provisioned storage. that you can create volumes from Spectrum Scale that are used as persistent storage for applications.

This publish describes how to configure IBM Spectrum Scale as a storage backend for POD volumes in IBM Cloud private clusters. whereas there are multiple ways to use a persistent quantity from IBM Spectrum Scale as a POD’s volume in IBM Cloud deepest, this publish describes a way to use the Hostpath and NFS drivers.

before you begin

In both scenarios(Hostpath and NFS), you need a IBM Spectrum Scale cluster and a persistent quantity. which you could additionally create the customized application used in this post the use of following steps.

  • Create a IBM Spectrum Scale cluster and designate a few nodes as IBM Cloud private employee nodes. during this instance, dghostrhel2, dghostrhel3 and dghostrhel4 are IBM Cloud private employee nodes
  • [root@dghostrhel2 ~]# mmlsclusterGPFS cluster suggestions========================GPFS cluster identify: dgcluster.dghost1GPFS cluster identity: 13644380956435301453GPFS UID area: dgcluster.dghost1Remote shell command: /usr/bin/sshRemote file reproduction command: /usr/bin/scpRepository type: CCR Node Daemon node name IP address Admin node name Designation— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — 1 dghost1 192.168.122.ninety six dghost1 quorum-manager2 dghost2 192.168.122.124 dghost2 quorum-manager3 dghost3 192.168.122.80 dghost3 quorum-manager4 dghost4 192.168.122.217 dghost4 quorum-manager9 dghostrhel1 192.168.122.221 dghostrhel110 dghostrhel2 192.168.122.222 dghostrhel211 dghostrhel3 192.168.122.223 dghostrhel312 dghostrhel4 192.168.122.224 dghostrhel413 dghostrhel5 192.168.122.225 dghostrhel5

    2. Create a quantity on the gpfs0 filesystem and mount it on the /gpfs/gpfs0 path.

    [root@dghostrhel1 ~]# mmlsfs gpfs0 flag value description— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —-f 8192 minimal fragment size in bytes-i 4096 Inode size in bytes-I 16384 oblique block size in bytes-d nsd1;nsd2 Disks in file device……-A yes automatic mount option-o none extra mount alternate options-T /gpfs/gpfs0 Default mount aspect— mount-priority 0 Mount precedence

    three. Create the custom software. The parameter values and file names that are used during this illustration are additionally utilized in later steps.

    3a. Create a directory named alpine4gpfs.

    3b. Create the software script in the alpine4gpfs directory. The script creates a directory in the Docker container and generates random variety of data in it. identify the file number_generator.sh

    #!/bin/sh

    #/gpfs is the set up listing from hostrandomnum=$(( ( RANDOM % 20 ) + 1 ))iodir=/gpfs/$HOSTNAME_$randomnumif [[ -f /gpfs/thisisgpfs ]]thenecho “Writing statistics into gpfs dir…”#Sleep for some time·sleep 10mkdir $iodirfor i in `seq 1 $randomnum`dodd if=/dev/urandom of=/$iodir/FILE$i bs=512K count number=10 &>/dev/nullmd5sum /$iodir/FILE$i > /$iodir/FILE$i.md5date > /$iodir/FILE$i.datedoneelseecho “No GPFS quantity..Exiting”fi

    note : The above script tests for /gpfs/thisisgpfs file inner the container earlier than generating data. therefore create the file ‘thisisgpfs’ internal fileset or nfs export which might be used as persistent quantity for the sample utility.

    3c. Create the dockerfile within the alpine4gpfs directory.

    FROM alpineMAINTAINER Your_nameADD number_generator.sh /number_generator.shRUN chmod +x /number_generator.shENTRYPOINT [“/number_generator.sh“]

    3d. Create the custom software. From the alpine4gpfs directory, run these commands:

    docker login mycluster.icp:8500docker build -t alpine4gpfs .docker tag alpine4gpfs mycluster.icp:8500/admin/alpine4gpfsdocker push mycluster.icp:8500/admin/alpine4gpfs

    observe : The above command add the photograph 'alpine4gpfs' in the 'admin' namespace. In case 'admin' namespace does not exist, The picture upload fails with the error message ’unauthorized: authentication required’. One will need to create the necessary namespace for efficiently uploading the image.Steps to create the ‘admin’ namespace :Login to IBM Cloud private >>control>>Namespaces>>click on “Create Namespace” then Create namespace with name ‘admin’

    using Storage from Spectrum Scale with IBM Cloud deepest by way of the Hostpath driver

    must haves for this illustration:

  • IBM Spectrum Scale Cluster
  • IBM Cloud inner most V2.1.0
  • that you would be able to choose to use some or all IBM Spectrum Scale client nodes as a IBM Cloud private employee nodes. If no longer all IBM Cloud private employee nodes are IBM Spectrum Scale valued clientele, then you need to use a node tag for those who use volumes that you just create from IBM Spectrum Scale. you could create volumes by using the listing or fileset instructions from the IBM Spectrum Scale File device. developing volumes by using fileset is really useful since it lets you partition the file equipment to enable administrative operations at a finer granularity than the whole file equipment. For more tips about fileset, see https://www.ibm.com/aid/knowledgecenter/STXKQY_4.2.three/com.ibm.spectrum.scale.v4r23.doc/bl1adv_filesets.htm.

    1. Create the fileset that the persistent extent uses

    1a. Create a fileset that is called ScaleCfCHostpath:

    [root@dghostrhel2 ~]# mmcrfileset gpfs0 ScaleCfCHostpath

    The output looks like this:

    Fileset ScaleCfCHostpath created with identification 2 root inode 33547.

    1b. hyperlink the fileset to the extent:

    [root@dghostrhel2 ~]# mmlinkfileset gpfs0 ScaleCfCHostpath -J /gpfs/gpfs0/ScaleCfCHostpath

    The output feels like this:

    Fileset ScaleCfCHostpath linked at /gpfs/gpfs0/ScaleCfCHostpath

    1c. screen the fileset details:

    [root@dghostrhel2 ~]# mmlsfileset gpfs0

    The output looks like this:

    Filesets in file gadget ‘gpfs0’:identify status Pathroot Linked /gpfs/gpfs0ScaleCfCHostpath Linked /gpfs/gpfs0/ScaleCfCHostpath

    The fileset ScaleCfCHostpath is installed on the /gpfs/gpfs0/ScaleCfCHostpath path. This path is used to create the extent in IBM Cloud inner most.

    2. Create the Persistent volume

    2a. Log in to the IBM Cloud deepest administration console.

    2b. From the menu, click on Platform > Storage.

    2c. click Create PersistentVolume.

    second. Enter the Persistent volume particulars. For this example, use here values:

    identify — spectrumscalestorageLabel — storageValue — spectrumscaleCapacity — 1Unit — GiAccess Mode — ReadWriteManyReclaim policy — RetainStorage classification — HostpathKey — path ; cost — /gpfs/gpfs0/ScaleCfCHostpath

    that you can provide these values by using the fields and lists in the Create New PersistentVolume window as proven in these images:

    Persistent quantity advent 1/3 Persistent quantity introduction 2/3 Persistent volume advent three/3

    2e. click on Create.

    2f. From the PersistentVolume tab, review the fame of the new persistent volume. earlier than you can use the persistent extent, its reputation need to be accessible.

    Persistent quantity details

    3. Create the Persistent quantity claim

    3a. click on the PersistentVolumeClaim tab.

    3b. click Create PersistentVolumeClaim.

    3c. Enter the quantity particulars. For this example, use here values:

    name — spectrumscalevolumeStorage Requests — 512Unit — MiAccess Mode — ReadWriteManyVolume Selector Label — storageValue — spectrumscale

    3d. click Create.

    3e. From the PersistentVolumeClaim tab, evaluation the repute of the brand new persistent quantity declare. The persistent quantity claim should have a standing of sure, which capacity that the persistent extent declare is sure to the persistent volume that contains the label storage=spectrumscale and meets its storage request requirements.

    Persistent extent declare details

    click on PersistentVolume and evaluate the alterations to the persistent volume ‘spectrumscalestorage’.

    Persistent quantity details

    that you may see that the persistent volume claim ‘ spectrumscalevolume’ is bound to the persistent extent ‘spectrumscalestorage’. The persistent extent declare ‘spectrumscalevolume’ is available to an application or pod.

    4. set up an application that uses the brand new volume claimA container is created for each illustration of the application. each and every container has a storage extent, and the quantity carries a directory that outlets the script’s random data.

    4a. From the IBM Cloud inner most console, click the menu and then click on Workloads.

    4b. From the Workloads page, click Jobs > Batch jobs.

    4c. click Create Job.

    4d. Enter the job particulars. You must supply values for a number of parameters:

    On the well-known tab, specify the job name and variety of situations:

    name — workloadwithalpineCompletions — 1

    On the Container Settings tab, specify the container name and the picture to use.

    name — alpinecontainerImage — mycluster.icp:8500/admin/alpine4gpfs

    On the Volumes tab, specify the following values:

    identify — spectrumscalevolumeVolume — spectrumscalevolumePath — /gpfs

    4e. evaluation the workloadwithalpine job particulars. The workloadwithalpine software with an connected extent should still be operating.

    Batch Job particulars Workload details

    in case you click the software name, you can see that situations of the workloadwithalpine utility is created.

    Pod particulars Container details

    each example of the software creates a listing of the identical name in connected extent. statistics generated by the applications is visible on Spectrum Scale consumers, as shown in the following instance:

    [root@dghostrhel2 ScaleCfCHostpath]# pwd/gpfs/gpfs0/ScaleCfCHostpath [root@dghostrhel2 ScaleCfCHostpath]# ls -1thisisgpfsworkloadwithalpine-kt4sn_17

    Q&A: IBM Flash Storage techniques CTO Andy walls | killexams.com Real Questions and Pass4sure dumps

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    IBM Raises The Bar For Storage, once again | killexams.com Real Questions and Pass4sure dumps

    The big information within the expertise world this past week became IBM organization’s buy of red Hat in one of the greatest utility enterprise acquisitions in history. whereas that is a basis-shaking movement for both IBM and purple Hat, it will not have an effect on the everyday lives of most working IT professionals.

    IT authorities focal point on providing continuously official and great service to their customers.  while the crimson Hat integration will take ages to yield up-to-date product and applied sciences roadmaps for the new combined business, IBM’s recent plethora of storage bulletins is plenty extra important when thinking about your IT firm’s needs.

    i like overlaying IBM's storage announcements. The storage neighborhood, while sitting inside an unquestionable behemoth of a know-how enterprise, moves with the tempo and agility of a a lot smaller company. it is a firm that has palpable power inside it.

    This agility and pace has allowed the IBM storage crew to carry a cadence of fabulous new technology and product bulletins. What I find captivating about this group is its steadfast alignment towards a compelling vision of how business statistics should be managed.

    IBM’s storage story makes a speciality of how information is generated, managed, and consumed inside an business. it is that figuring out that generates a collection of technologies that may also be leveraged to deliver a cohesive answer to any IT company.

    Would you've got guessed that IBM now has the broadest portfolio of NVMe and NVMe-over-material enabled products within the industry? It shocked me. I wasn’t shocked to learn that IBM is the world leader in tape archive solutions, considering the fact that I all started in this industry by using altering nine-track tapes for gas cash. IBM and tape are completely cemented in my intellect. on the same time, a portfolio that reaches from arguably the fastest storage arrays in construction to the lowliest tape power is rather a span.

    All about utility

    IBM has always been about delivering cohesive software-first options to IT, which it carefully couples with smartly-engineered hardware. This has been actual when you consider that IBM delivered its first mainframe seventy years ago, carrying on with these days with its wide range of choices in compute and storage that span on-premises and cloud architectures.

    as it groups movement from tape to cloud for data insurance policy and archival storage, and as information comes alive with the rise of AI-driven analytics and aspect-driven compute, managing that information becomes a logistical challenge. knowing where facts is, what it’s used for, and what the corporation’s necessities are in retaining that facts is what drives long-term technology decisions.

    Managing the circulation of an organization’s facts, whereas providing insights on the traits of that records, is what IBM’s Spectrum Storage suite is all about. There are IBM Spectrum Storage products that manage information in virtualized environments, provision storage into hybrid-cloud deployments, control the complexities of facts-insurance plan and backup, and that supply utility-described storage solutions for file, block, and objects.

    IBM's fresh bulletins included a flurry of recent facets throughout the Spectrum Storage line, together with a new solution for SAP HANA installations that leverages IBM storage, IBM Spectrum give protection to, and IBM Spectrum replica. It’s a big checklist, however two application-connected announcements dominated my attention.

    IBM Storage Insights

    IBM Storage Insights is a cloud-based management tool that leverages IBM AI technology to detect storage networking efficiency concerns and proactively generate guide cases to aid IT before concerns turn into issues. This type of skill is rolling out from carriers across the IT trade and is quickly fitting table stakes for selling storage options into the commercial enterprise.

    i'm glad to look IBM unencumber this product. Given IBM's heritage in synthetic intelligence, mixed with its half-century-long institutional legacy in aiding datacenter know-how, it might become anything special.

    Mapping unstructured statistics

    Analytics-pushed workloads and part computing each pressure new necessities for unstructured facts. Object storage is one of the quickest turning out to be storage applied sciences in enterprise IT, with solutions from most precise-tier storage corporations, together with IBM. The problem of unstructured statistics lies in the potential to bear in mind what the records is, where it got here from, and what an IT group should still do with it.

    IBM Spectrum discover is a brand new providing from IBM that goals to tame unstructured records and provide insights into precisely what that statistics is. Spectrum find, which arose from technology generated inner of IBM research, scans and creates metadata, growing catalogs for an unstructured facts set. here's designed to give a roadmap to an organization's statistics. IBM Spectrum discover will guide information inside IBM Cloud Object Storage and IBM Spectrum Scale programs and, most exceedingly, is expected to guide Dell EMC Isilon sometime in 2019.

    realizing unstructured statistics is a challenge for businesses, one that turns into extra elaborate each day. It’s additionally no longer an easy problem to install traditional application tools to resolve. It’s extremely good to look IBM carry know-how into this area. it should set the bar for others to follow.

    efficiency and density

    As excited as I get about software, it is simply pretty much as good because the underlying hardware. IT consumers today should still suppose spoiled by means of the satisfactory and breadth of solutions accessible to them. the upward thrust of strong-state storage, NVMe interconnects, and integrated server-classification processing makes it an excellent time to buy storage options.

    IBM is expanding its portfolio of hardware offerings and, on the identical time, becoming the expertise provider with the broadest providing of NVMe and NVMe-over-cloth storage products. IBM focuses on performance with NVMe, and on storage density with its pleasing flash packaging technology.

    The most reliable illustration of IBM’s focus on density is the DS8880F, which basically doubles its flash potential to a whopping 737TB (compared to its previous 368TB). at the same time, the IBM FlashSystem 900 and 9100 items are doubling in skill with new 18TB modules as a way to assist as much as 44TB positive skill. This new means is up from 22TB provided via the previous generation module.

    The updated third-generation Storwize V7000 gains built-in NVMe and various enlargement alternatives, while providing what IBM describes as a 2.7x boost in highest throughput for some workloads—all whereas being clustered with different V7000 arrays to serve a cluster-vast 32PB of skill.

    IBM introduced extra hardware features and tweaks across its storage line. check out its site if you are looking to go deep on the details.

    Concluding recommendations

    within the fight for bragging rights, vendors post "hero numbers" that display that they've the world's fastest array. This yr that claim become credibly made by means of Dell EMC, Pure Storage, and IBM. The reality is that most platforms that are constructed on up to date-technology—leveraging NVMe interconnects both inner and outside of the field, and wearing reputable media—will all satisfy most annoying storage wants.

    past hardware bragging rights, IT teams need to focal point on the broader query of how these storage options are integrated into their usual IT architecture. application is the glue that ties together storage hardware with cloud, hybrid-cloud, and other storage and compute models to service the changing demands of statistics in the enterprise.

    IBM specializes in offering a holistic solution to datacenters. Given the breadth and depth of its storage portfolio, its helping application suites, and its penetration into cloud, IBM is one to watch as firms’ bits and bytes are turned into actionable facts.

    Disclosure: My enterprise, Moor Insights & approach, like any research and analyst companies, offers or has supplied analysis, evaluation, advising, and/or consulting to many excessive-tech agencies in the business, including Dell EMC, crimson Hat, and IBM, which can be referred to listed here. I don't cling any equity positions with any businesses noted during this column.


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    High Volume Storage Fundamentals V3

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    Building a Graph Database on a Key-Value Store? | killexams.com real questions and Pass4sure dumps

    [Excerpted from the eBook Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link Analytics]

    Until recently, graph database designs fulfilled some but not all of the graph analytics needs of enterprises. The first generation of graph databases (e.g., Neo4j) was not designed for big data. They cannot scale out to a distributed database, are not designed for parallelism, and perform slowly at both data loading and querying for large datasets and/or multi-hop queries.

    The second generation of graph databases (e.g., DataStax Enterprise Graph) was built on top of NoSQL storage systems such as Apache Cassandra. This addressed the scale-out issue, but since they lack a native graph storage design, they still have slow performance for graph updates or multi-hop queries on large datasets. Faced with the apparent lack of a high-performance graph database solution, some organizations have considered building their own in-house graph solution. Rather than starting from scratch, a modern fast key-value store seems like a good base on which to build a custom graph solution.

    The Lure of Key-Value Stores

    The NoSQL revolution came about because relational databases were too slow and too inflexible. Big data users needed to ingest a variety of differently structured data, with both high volume and high velocity, and to scale out their physical infrastructure with minimal fuss. The key-value store arose as the simplest and therefore fastest NoSQL architecture. A key-value database is basically a two-column hash table, where each row has a unique key (ID) and a value associated with that key. Searching the key field can return single data values very quickly, much faster than a relational database. Key-value stores also scale well to very large data sets. Key-value stores are well understood, and many free open-source designs are available. It seems fairly straightforward to use a fast key-value store to hold a graph’s node entities and edge entities, implement a little more logic and call that a graph database.

    The Real Cost of Building Your Own Graph Database

    Building your own graph database on top of a key-value store, let alone a high-performance one, is much harder than it looks. Here are two obvious difficulties:

  • You are following the architectural principles of the 2nd generation graph databases, but you are trying to improve upon those. Products such as DataStax Enterprise Graph have already optimized performance as much as they can, given the inherent limitations of a non-native graph storage design.
  • What you really need is a complete database management system (DBMS), not just a graph database. A database is just a collection of data organized in a specific way such that storing and retrieving data are supported. A DBMS, on the other hand, is the complete application program for using and managing data via a database. A DBMS typically includes support for defining a database model, inserting, reading, updating, and deleting data, performing computational queries (not just reading the raw data), applying data integrity checks, supporting transactional guarantees and concurrency, interacting with other applications, and performing administrative functions, such as managing user privileges and security. A do-it-yourself design will need to implement and verify all of these additional requirements.
  • Let’s examine some of the common problems in building a high-performance graph on top of key-value storage.

  • Data inconsistency and wrong query results
  • Architectural mismatch with slow performance
  • Expensive and rigid implementation
  • Lack of enterprise support
  • 1. Data Inconsistency and Wrong Query Results 

    Key-value stores are typically highly optimized for high throughput and low latency of single-record reads/writes. As a result, ACID (Atomicity, Consistency, Isolation, Durability) properties are guaranteed only at the single-record level for most key-value stores. This means that data consistency is at risk for any real-world transactional events that involve the writing of multiple records (e.g., customer purchases, money transfers, a rideshare request). Lack of data consistency means that queries that aggregate across two or more of these common multi-record transactions can result in incorrect results.

    Developers hoping to build a system that businesses can truly trust should realize that data consistency and guaranteeing correct results is an all-or-nothing endeavor. There are no shortcuts for ACID properties. In a graph system, one transaction will often need to create multiple nodes and add new connections/edges between new or existing nodes, all within a single, all-or-nothing transaction. The easy way to guarantee ACID is to perform only one operation at a time, locking out all other operations. But this results in very slow throughput.

    In a high-performance design, a transaction must never interfere with, slow down, or block any other existing or incoming read-only queries, even if they are traversing neighboring parts of the graph. If concurrent operations are a business requirement, then developers need to design and engineer a non-blocking graph transactional architecture where multiple key-value transaction support is added inside the distributed key-value system. There are many stories of development teams trying to implement a shortcut workaround that inevitably led to downstream data and query correctness issues.

    2. Architectural Mismatch With Slow Performance

    Queries involving single records are fast with a key-value store. But key-value store query speeds degrade sharply as queries become more complex. With each hop in the graph traversal, the application has to send a key-value retrieval command to the external key-value store. More hops generate more communication overhead and result in slower query performance. And computation on the data, such as filtering, aggregation, and evidence-gathering, cannot be completely pushed down to the key-value store. Instead, it must take place in the graph application layer, which creates additional input and output transmission with the key-value store while bogging down the query performance even more. Moreover, computing in the graph application layer instead of inside a native parallel graph platform is by default non-parallel. To match the parallelism in a Native Parallel Graph like TigerGraph, developers would need to take on the extra burden of parallelizing the computation in a single machine or take the even more extraordinary burden of scaling out the computation to multiple machines and taking care of the scheduling, messaging, failover, and other requirements of a distributed system.

    Enterprises that want a high-performance graph want a system that analyzes complex relationships and surfaces hidden patterns, with fast query performance. To address these requirements, developers would need to do all of the following: build a graph over key-value application that handles basic graph modeling (describing the structure and behavior of a graph in general), implement basic graph data definition and data manipulation operations, ensure transactional guarantees, and then develop application queries on top of that. That is, in-house developers would need to implement an interpreter for a data definition language (DDL) and a graph query language. Developers should consider the significant time and resources required to support such a challenging endeavor and expect to deal with a lot of unforeseen downstream problem-solving.

    3. Expensive and Rigid Implementation

    Organizations thinking about creating their own graph database system on top of another database system should consider the resources needed, reliability desired, and ideal adaptability of their application.

    Building a graph on key-value system would take a great deal of time to complete. The upfront costs of building custom software should not be underestimated. Furthermore, reliability is critical to application success. As previously discussed, developers should put great thought into how they will design, test, deploy, and support a graph ecosystem that includes full data consistency, correct results, and high performance of complex queries. Finally, management and development teams should consider that a single application likely doesn’t have the general purpose capabilities and flexibility of a high-performance distributed native graph database product. Decision makers should consider the big picture and whether their custom design project can address unforeseen future expansion. By choosing the right graph capabilities now, you can streamline tasks, reduce errors, and make everyone’s life a lot easier for years to come.

    4. Lack of Enterprise Support

    Last but not least, a custom design will not include many of the features that are crucial for secure and reliable enterprise deployment: user authorization and access control, logging, high availability, and integration with other applications. Some of these enterprise features might be available for the key-value store, but they need to be available at the graph (application) level.

    Example: Graph Update for One Event

    The following example shows the actions that occur in a typical graph database update. It illustrates how data inconsistency and incorrect results can occur when using a key-value store based graph system and discusses the development challenges and limitations in trying to build functional workarounds.

    Consider this update scenario. A real-time event takes place where one user transfers money to another user using a bank card on a mobile phone. Such transactions can be modeled with a simple graph schema, as shown below.

    transaction schema

    Figure 1: Graph Schema for Electronic Payments

    This example applies to many other use cases. For example, you can replace the money transfer event with a product purchase event or a service engagement event, such as hiring a ridesharing service.

    The money transfer Event e1 is a node which links together four related entities  (u1, u2, c1, p1), where:

  • u1 is the User sending money,
  • u2 is the User receiving money,
  • c1 is the payment Card used, and
  • p1 is the Phone used.
  • For simplicity, we ignore all other attributes present in this event (such as dollar amount, location, and timestamp).

    There are multiple ways to model a graph database with a graph over key-value system. One approach is to have two tables in the graph over key-value system: a node table V and an edge table E. Another approach is to just have a single node table (no edge table) which has a row for each node to store both node attributes and edge information (edge attributes and connected nodes). The challenges faced in either approach are similar, so we use the more straightforward two-table approach.

    To update the graph by adding a new Event e1, we perform the following actions:

  • Create a new Event node e1. Equivalently in the graph over key-value system approach, we need to insert one row in the node table V.
  • For each entity u1, u2, c1, and p1, check if it already exists. Create a new node for each one that does not yet exist. Equivalently, key-value system approach, we need to insert from zero to four rows in the node table V.
  • Add four edges between e1 and the four other entities: (e1,u1), (e1,u2), (e1,c1), and e1,p1). Equivalently, add four rows to the key-value system’s edge table E. This will directly connect all cards and phones used by a user but would make a graph over key-value system implementation even more complex and error-prone. In the equivalent key-value store, we need to insert four rows in the edge table E.Also we want to be able to find all events connected to a phone, a user, or a card. This requires ensuring that we can traverse connections in either direction. Depending on the graph implementation, we either need to mark the edges as bidirectional or we need to add four additional edges in the other direction: (u1,e1), (u2,e1), (c1,e1), and (p1,e1). The equivalent key-value system approach would need to insert four more rows in the edge table E.
  • In total, one new Event requires creating one to five nodes and four to eight edges. The following figure depicts three events which have some Users in common:

    transaction update graph details

    Transaction Update Graph Details

    Since the Event e1 is treated as a single transaction, the above three actions need to happen to the graph (equivalently to two tables) in an atomic way, which means that:

  • Either all three actions are guaranteed to take place fully, or none of them should take place. A non-transactional system which allows some but not all of the actions to take place (e.g., action 1 and 2 complete successfully but action 3 does not) leads to data inconsistency and wrong results. If any one or more actions do not complete, the result will be missing edges with wrong query results.  For this graph model and event, the three actions contain up to 13 nodes and edges to add.
  • During the time we apply actions 1, 2 and 3 and before they are completed, their effect should not be visible to any other read or write operations. This means that if you have another query q1, which is still traversing the edges/nodes in the graph before the event e1 comes into the system, q1 should not see any of the actions of 1, 2 and 3 even if they complete successfully before e1 finishes.
  • This also means that if a read-only query q2 comes into the system later than e1 but before the three actions of e1 are all completed, the effects of e1 should not be visible to q2.
  • Possible Solutions to Graph Over Key-Value Shortcomings

    A workaround is to simply ignore all data inconsistency and wrong query result issues and just insert key-value rows to node table V and edge table E and hope for the best. Even if a company’s business requirements can be relaxed to tolerate data inconsistency and wrong query results, this does not remove the implementation challenge. That is because the users don’t know whether or when they will miss a node or edge in the graph. To address this, developers will need to tack on protection code to ensure the application will not hang or crash due to traversing ghost/missing edges.

    A better solution would be to implement true support for concurrent multi-step transactions inside the distributed key-value system. But this requires not only a huge engineering effort but also significant slowdown of key-value operations.

    A third solution, a “placebo solution,” is to add some kind of locking mechanism inside the application level without changing the kernel of the underlying key-value store. Workarounds, like attempting to add a “commit” flag to each row or maintaining a “dirty” list of rows in their applications, can reduce data inconsistencies and incorrect results. However, such add-on features are not equivalent to an architecture designed for transactional guarantees, so data inconsistencies and incorrect results may persist albeit hidden. Ensuring that these workarounds consistently reconcile and address each transaction can be very difficult to guarantee.

    Summary

    In summary, designing an application-level graph on top of a key-value store is an expensive and complex job which will not yield a high-performance result. While key-value stores excel at single key-value transactions, they lack ACID properties and the complex transaction functionality required by a graph update. Thus, building a graph database on top of a key-value store opens the door to data inconsistencies, wrong query results, slow query performance for multi-hop queries, and an expensive and rigid deployment.


    What’s Behind China’s Record Oil Imports? | killexams.com real questions and Pass4sure dumps

    China imported a record-high volume of crude oil in October, beating the previous all-time high set in April this year. The oil market, gripped by a strong bearish sentiment last week, largely ignored the Chinese data.

    Crude oil imports at the world’s top importer will continue to grow, but last month’s record was determined not only by fundamentals—it was influenced by one-off shorter-term factors that boosted oil intake, according to Reuters columnist Clyde Russell.

    Chinese crude oil imports continue to be propped up by economic growth at over 6 percent, which, however, has slowed down in recent months with the U.S.-China trade war. Other long-term factors driving oil demand growth include declining domestic crude oil production with mature fields depleting and the building up of strategic petroleum reserves.

    Yet, in October two short-term factors also contributed to the record Chinese crude oil imports—the U.S. sanctions on Iran and the approaching expiry date for China’s smaller independent refiners to use up their 2018 oil import quotas. Various estimates show that China significantly boosted its crude oil imports from Iran last month, just before the U.S. sanctions snapped back on November 5 and before the announcement that waivers were granted, and before independent refiners—the so-called teapots—likely imported a record high volume of crude oil in October to fully use their quotas for this year.

    So, going forward, China’s total crude oil imports may not continue to break record highs, as demand from teapots will likely ease, and as there won’t be such a rush for Iranian oil, all the more so that China was among the eight countries granted waivers by the U.S. to continue importing Iran’s oil, at least until May next year.

    Among the factors underpinning China’s continued and long-term oil demand growth are declining domestic production and an expanding economy, although GDP is now rising at a slower pace than earlier this year. Related: Iran’s Army Vows To Protect Oil Tankers From Threats

    China’s crude oil production in January-September dropped by 1.9 percent compared to the same period a year earlier, according to data from the National Bureau of Statistics of China. In September, crude oil production in China fell 2.4 percent compared to September 2017.

    China’s economy, for its part, expanded by 6.7 percent annually at comparable prices between January and September, the statistics data showed. However, the pace of growth has been slowing this year, from 6.8 percent in Q1, to 6.7 percent in Q2, and to 6.5 percent in Q3, with third-quarter growth at its slowest expansion rate since the peak of the global financial crisis in 2009.

    China has started to introduce fiscal stimulus to prop up its economy. The stimulus could boost China’s imports in all sectors in 2019, according to ING, which sees China’s exports growth in October as a possible sign that Chinese exporters are concerned that more U.S. tariffs are coming next year. ING doesn’t think that the Trump-Xi meeting at the G20 summit in Argentina later this month will yield positive results, Iris Pang, Economist, Greater China, at ING, wrote in an analysis last week.

    Factors driving the record-high Chinese oil imports in October included record purchases from the teapots and high imports from Iran. Independent refiners imported nearly 2 million bpd of China’s total 9.61 million bpd imports in October, according to Refinitiv Oil Research and Forecasts. Related: The Biggest Threat To Dollar Dominance

    Refinitiv data also show that China’s oil imports from Iran jumped to 654,000 bpd last month from 458,000 bpd in September.

    According to S&P Global Platts trade flow data, Iran’s oil shipments to China between October 1 and 21 averaged 800,000 bpd, up from around 600,000 bpd average for September. About half of China’s crude oil and condensate imports from Iran in October—around 400,000 bpd—were bound for a storage hub in Dalian in northeastern China, according to Platts sources and shipping data. The National Iranian Oil Company (NIOC) has reportedly leased some storage capacity at Dalian.

    Iranian oil flows may become even trickier to track with sanctions now in force and Iran reportedly switching off transponders on board ships. So data may become less and less reliable as to how much Iranian oil Tehran’s top oil customer—China—is importing.

    Economic growth and domestic oil production decline will continue to drive Chinese crude imports, but it could be a while until China beats the October record.

    By Tsvetana Paraskova for Oilprice.com

    More Top Reads From Oilprice.com:


    NetApp Earnings Preview: Trying To Keep The Momentum | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!Computer storage company NetApp (NTAP) has been performing extremely well over the past year, both in terms of the fundamentals and ... current P/E ratio is really high at 95, but the forward ...


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